{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/tevfikmuratyildirim/Dropbox/RESEARCH/A. T. Bulut & T. M. Yildirim/Survey Experiment - Konda/Elite Influence on Gender Egalitarianism--Replication.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 6 May 2021, 20:56:52
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/4_/2pllzwnn7q3b3r7q838pffsr0000gn/T//SD39641.000000"
{txt}
{com}. * Figure 1
. gen equalofopportunity_abscorrect = (equalofopportunity==5)
{txt}
{com}. 
. ***
. graph hbar equalofopportunity_abscorrect if AKP==1, scheme(s1mono) over(treatment, relabel(1 "Control" 2 "Treatment")) name(AKP, replace)
{res}{txt}
{com}. graph hbar equalofopportunity_abscorrect if CHP==1, scheme(s1mono) over(treatment, relabel(1 "Control" 2 "Treatment")) name(CHP, replace)
{res}{txt}
{com}. graph hbar equalofopportunity_abscorrect if fem==1, scheme(s1mono) over(treatment, relabel(1 "Control" 2 "Treatment")) name(female, replace)
{res}{txt}
{com}. graph hbar equalofopportunity_abscorrect if college==1, scheme(s1mono) over(treatment, relabel(1 "Control" 2 "Treatment")) name(college, replace)
{res}{txt}
{com}. 
. graph combine AKP CHP college female, scheme(s1mono) xcommon cols(2)
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/4_/2pllzwnn7q3b3r7q838pffsr0000gn/T//SD39641.000000"
{txt}
{com}. 
. * Figure 2
. gen womenpolitics_abswrong = (womenpolitics==1)
{txt}
{com}. 
. ***
. graph hbar womenpolitics_abswrong if AKP==1, scheme(s1mono) over(treatment, relabel(1 "Control" 2 "Treatment")) name(AKP_2, replace)
{res}{txt}
{com}. graph hbar womenpolitics_abswrong if CHP==1, scheme(s1mono) over(treatment, relabel(1 "Control" 2 "Treatment")) name(CHP_2, replace)
{res}{txt}
{com}. graph hbar womenpolitics_abswrong if fem==1, scheme(s1mono) over(treatment, relabel(1 "Control" 2 "Treatment")) name(female_2, replace)
{res}{txt}
{com}. graph hbar womenpolitics_abswrong if college==1, scheme(s1mono) over(treatment, relabel(1 "Control" 2 "Treatment")) name(college_2, replace)
{res}{txt}
{com}. 
. graph combine AKP_2 CHP_2 college_2 female_2, scheme(s1mono) xcommon cols(2)
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/4_/2pllzwnn7q3b3r7q838pffsr0000gn/T//SD39641.000000"
{txt}
{com}. 
. 
. ****** Table 2
. ologit equalofopportunity treatment

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3034.4697}  
Iteration 1:{space 3}log likelihood = {res:-3031.6586}  
Iteration 2:{space 3}log likelihood = {res:-3031.6582}  
Iteration 3:{space 3}log likelihood = {res:-3031.6582}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     2,714
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      5.62
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0177
{txt}Log likelihood = {res}-3031.6582{txt}{col 49}Pseudo R2{col 67}= {res}    0.0009

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}equalofopportunity{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}treatment {c |}{col 20}{res}{space 2}-.1722532{col 32}{space 2} .0726741{col 43}{space 1}   -2.37{col 52}{space 3}0.018{col 60}{space 4}-.3146918{col 73}{space 3}-.0298146
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2}-4.852994{col 32}{space 2} .2130083{col 60}{space 4}-5.270483{col 73}{space 3}-4.435505
{txt}             /cut2 {c |}{col 20}{res}{space 2} -2.77022{col 32}{space 2} .0872469{col 60}{space 4}-2.941221{col 73}{space 3} -2.59922
{txt}             /cut3 {c |}{col 20}{res}{space 2}-1.924679{col 32}{space 2} .0672954{col 60}{space 4}-2.056576{col 73}{space 3}-1.792783
{txt}             /cut4 {c |}{col 20}{res}{space 2} .1681358{col 32}{space 2} .0528441{col 60}{space 4} .0645632{col 73}{space 3} .2717084
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit equalofopportunity treatment if AKP==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1162.9668}  
Iteration 1:{space 3}log likelihood = {res:-1160.0987}  
Iteration 2:{space 3}log likelihood = {res:-1160.0972}  
Iteration 3:{space 3}log likelihood = {res:-1160.0972}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       947
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      5.74
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0166
{txt}Log likelihood = {res}-1160.0972{txt}{col 49}Pseudo R2{col 67}= {res}    0.0025

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}equalofopportunity{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}treatment {c |}{col 20}{res}{space 2}-.2907136{col 32}{space 2} .1215239{col 43}{space 1}   -2.39{col 52}{space 3}0.017{col 60}{space 4} -.528896{col 73}{space 3}-.0525312
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2} -4.70289{col 32}{space 2} .3255182{col 60}{space 4}-5.340894{col 73}{space 3}-4.064886
{txt}             /cut2 {c |}{col 20}{res}{space 2} -2.36414{col 32}{space 2} .1282148{col 60}{space 4}-2.615436{col 73}{space 3}-2.112843
{txt}             /cut3 {c |}{col 20}{res}{space 2}-1.491519{col 32}{space 2}  .103817{col 60}{space 4}-1.694997{col 73}{space 3}-1.288042
{txt}             /cut4 {c |}{col 20}{res}{space 2} .5454621{col 32}{space 2} .0917889{col 60}{space 4} .3655592{col 73}{space 3}  .725365
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit equalofopportunity treatment if CHP==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-427.56687}  
Iteration 1:{space 3}log likelihood = {res:-426.06568}  
Iteration 2:{space 3}log likelihood = {res: -426.0647}  
Iteration 3:{space 3}log likelihood = {res: -426.0647}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       523
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      3.00
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0830
{txt}Log likelihood = {res} -426.0647{txt}{col 49}Pseudo R2{col 67}= {res}    0.0035

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}equalofopportunity{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}treatment {c |}{col 20}{res}{space 2}-.3127413{col 32}{space 2} .1807196{col 43}{space 1}   -1.73{col 52}{space 3}0.084{col 60}{space 4}-.6669452{col 73}{space 3} .0414625
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2}-6.423835{col 32}{space 2}  1.00593{col 60}{space 4}-8.395422{col 73}{space 3}-4.452247
{txt}             /cut2 {c |}{col 20}{res}{space 2}-4.329274{col 32}{space 2} .3697694{col 60}{space 4}-5.054009{col 73}{space 3}-3.604539
{txt}             /cut3 {c |}{col 20}{res}{space 2}-3.288872{col 32}{space 2} .2389163{col 60}{space 4}-3.757139{col 73}{space 3}-2.820604
{txt}             /cut4 {c |}{col 20}{res}{space 2}-.7259749{col 32}{space 2} .1300372{col 60}{space 4}-.9808431{col 73}{space 3}-.4711066
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit equalofopportunity treatment fem religiosity eduord age age2 urbanrural income econ_wellbeing  

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -2658.166}  
Iteration 1:{space 3}log likelihood = {res:-2545.6015}  
Iteration 2:{space 3}log likelihood = {res:-2544.8879}  
Iteration 3:{space 3}log likelihood = {res:-2544.8873}  
Iteration 4:{space 3}log likelihood = {res:-2544.8873}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     2,402
{txt}{col 49}LR chi2({res}9{txt}){col 67}= {res}    226.56
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2544.8873{txt}{col 49}Pseudo R2{col 67}= {res}    0.0426

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}equalofopportunity{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}treatment {c |}{col 20}{res}{space 2}-.2058202{col 32}{space 2} .0792434{col 43}{space 1}   -2.60{col 52}{space 3}0.009{col 60}{space 4}-.3611345{col 73}{space 3} -.050506
{txt}{space 15}fem {c |}{col 20}{res}{space 2} .7059406{col 32}{space 2}  .082719{col 43}{space 1}    8.53{col 52}{space 3}0.000{col 60}{space 4} .5438142{col 73}{space 3} .8680669
{txt}{space 7}religiosity {c |}{col 20}{res}{space 2}-.3547097{col 32}{space 2} .0635638{col 43}{space 1}   -5.58{col 52}{space 3}0.000{col 60}{space 4}-.4792925{col 73}{space 3} -.230127
{txt}{space 12}eduord {c |}{col 20}{res}{space 2} .2130905{col 32}{space 2}  .035884{col 43}{space 1}    5.94{col 52}{space 3}0.000{col 60}{space 4} .1427591{col 73}{space 3} .2834218
{txt}{space 15}age {c |}{col 20}{res}{space 2}-.0528158{col 32}{space 2} .0144033{col 43}{space 1}   -3.67{col 52}{space 3}0.000{col 60}{space 4}-.0810459{col 73}{space 3}-.0245858
{txt}{space 14}age2 {c |}{col 20}{res}{space 2} .0005647{col 32}{space 2} .0001555{col 43}{space 1}    3.63{col 52}{space 3}0.000{col 60}{space 4} .0002599{col 73}{space 3} .0008695
{txt}{space 8}urbanrural {c |}{col 20}{res}{space 2} .0384492{col 32}{space 2} .0553277{col 43}{space 1}    0.69{col 52}{space 3}0.487{col 60}{space 4} -.069991{col 73}{space 3} .1468895
{txt}{space 12}income {c |}{col 20}{res}{space 2} .1364111{col 32}{space 2} .0444225{col 43}{space 1}    3.07{col 52}{space 3}0.002{col 60}{space 4} .0493446{col 73}{space 3} .2234776
{txt}{space 4}econ_wellbeing {c |}{col 20}{res}{space 2} .0993035{col 32}{space 2} .0451342{col 43}{space 1}    2.20{col 52}{space 3}0.028{col 60}{space 4} .0108421{col 73}{space 3}  .187765
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2}-5.273407{col 32}{space 2} .5095428{col 60}{space 4}-6.272092{col 73}{space 3}-4.274721
{txt}             /cut2 {c |}{col 20}{res}{space 2}-3.113492{col 32}{space 2} .4619024{col 60}{space 4}-4.018805{col 73}{space 3} -2.20818
{txt}             /cut3 {c |}{col 20}{res}{space 2}-2.265251{col 32}{space 2} .4581166{col 60}{space 4}-3.163143{col 73}{space 3}-1.367359
{txt}             /cut4 {c |}{col 20}{res}{space 2}-.0176577{col 32}{space 2}  .455745{col 60}{space 4}-.9109015{col 73}{space 3} .8755861
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit equalofopportunity treatment fem religiosity eduord age age2 urbanrural income econ_wellbeing if AKP==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1048.9945}  
Iteration 1:{space 3}log likelihood = {res: -1021.847}  
Iteration 2:{space 3}log likelihood = {res:-1021.7035}  
Iteration 3:{space 3}log likelihood = {res:-1021.7034}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       862
{txt}{col 49}LR chi2({res}9{txt}){col 67}= {res}     54.58
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1021.7034{txt}{col 49}Pseudo R2{col 67}= {res}    0.0260

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}equalofopportunity{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}treatment {c |}{col 20}{res}{space 2}-.3454019{col 32}{space 2} .1289376{col 43}{space 1}   -2.68{col 52}{space 3}0.007{col 60}{space 4} -.598115{col 73}{space 3}-.0926888
{txt}{space 15}fem {c |}{col 20}{res}{space 2} .7432234{col 32}{space 2} .1373845{col 43}{space 1}    5.41{col 52}{space 3}0.000{col 60}{space 4} .4739547{col 73}{space 3} 1.012492
{txt}{space 7}religiosity {c |}{col 20}{res}{space 2}-.3147745{col 32}{space 2} .1159124{col 43}{space 1}   -2.72{col 52}{space 3}0.007{col 60}{space 4}-.5419587{col 73}{space 3}-.0875903
{txt}{space 12}eduord {c |}{col 20}{res}{space 2} .0832528{col 32}{space 2} .0623427{col 43}{space 1}    1.34{col 52}{space 3}0.182{col 60}{space 4}-.0389366{col 73}{space 3} .2054423
{txt}{space 15}age {c |}{col 20}{res}{space 2}-.0513301{col 32}{space 2} .0251755{col 43}{space 1}   -2.04{col 52}{space 3}0.041{col 60}{space 4}-.1006733{col 73}{space 3} -.001987
{txt}{space 14}age2 {c |}{col 20}{res}{space 2} .0005274{col 32}{space 2} .0002661{col 43}{space 1}    1.98{col 52}{space 3}0.047{col 60}{space 4} 5.93e-06{col 73}{space 3} .0010489
{txt}{space 8}urbanrural {c |}{col 20}{res}{space 2}-.1408254{col 32}{space 2} .0906869{col 43}{space 1}   -1.55{col 52}{space 3}0.120{col 60}{space 4}-.3185685{col 73}{space 3} .0369176
{txt}{space 12}income {c |}{col 20}{res}{space 2} .1378942{col 32}{space 2} .0750532{col 43}{space 1}    1.84{col 52}{space 3}0.066{col 60}{space 4}-.0092074{col 73}{space 3} .2849957
{txt}{space 4}econ_wellbeing {c |}{col 20}{res}{space 2} .0859478{col 32}{space 2} .0826383{col 43}{space 1}    1.04{col 52}{space 3}0.298{col 60}{space 4}-.0760204{col 73}{space 3} .2479159
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2}-6.122124{col 32}{space 2} .9018289{col 60}{space 4}-7.889676{col 73}{space 3}-4.354572
{txt}             /cut2 {c |}{col 20}{res}{space 2}-3.655549{col 32}{space 2} .8358596{col 60}{space 4}-5.293804{col 73}{space 3}-2.017294
{txt}             /cut3 {c |}{col 20}{res}{space 2}  -2.7672{col 32}{space 2} .8314099{col 60}{space 4}-4.396734{col 73}{space 3}-1.137667
{txt}             /cut4 {c |}{col 20}{res}{space 2}-.6441153{col 32}{space 2} .8262735{col 60}{space 4}-2.263582{col 73}{space 3}  .975351
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit equalofopportunity treatment fem religiosity eduord age age2 urbanrural income econ_wellbeing if CHP==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-380.94876}  
Iteration 1:{space 3}log likelihood = {res:-354.20148}  
Iteration 2:{space 3}log likelihood = {res:-353.76202}  
Iteration 3:{space 3}log likelihood = {res:-353.76131}  
Iteration 4:{space 3}log likelihood = {res:-353.76131}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       471
{txt}{col 49}LR chi2({res}9{txt}){col 67}= {res}     54.37
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-353.76131{txt}{col 49}Pseudo R2{col 67}= {res}    0.0714

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}equalofopportunity{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}treatment {c |}{col 20}{res}{space 2}-.3525676{col 32}{space 2}  .200329{col 43}{space 1}   -1.76{col 52}{space 3}0.078{col 60}{space 4}-.7452053{col 73}{space 3} .0400701
{txt}{space 15}fem {c |}{col 20}{res}{space 2} .9462729{col 32}{space 2} .2100523{col 43}{space 1}    4.50{col 52}{space 3}0.000{col 60}{space 4} .5345779{col 73}{space 3} 1.357968
{txt}{space 7}religiosity {c |}{col 20}{res}{space 2}-.2723232{col 32}{space 2} .1620286{col 43}{space 1}   -1.68{col 52}{space 3}0.093{col 60}{space 4}-.5898935{col 73}{space 3} .0452472
{txt}{space 12}eduord {c |}{col 20}{res}{space 2} .2617173{col 32}{space 2}  .091712{col 43}{space 1}    2.85{col 52}{space 3}0.004{col 60}{space 4} .0819651{col 73}{space 3} .4414696
{txt}{space 15}age {c |}{col 20}{res}{space 2} -.002451{col 32}{space 2} .0344609{col 43}{space 1}   -0.07{col 52}{space 3}0.943{col 60}{space 4}-.0699931{col 73}{space 3} .0650912
{txt}{space 14}age2 {c |}{col 20}{res}{space 2}-6.31e-06{col 32}{space 2} .0003657{col 43}{space 1}   -0.02{col 52}{space 3}0.986{col 60}{space 4}-.0007231{col 73}{space 3} .0007105
{txt}{space 8}urbanrural {c |}{col 20}{res}{space 2} .1542353{col 32}{space 2} .1333437{col 43}{space 1}    1.16{col 52}{space 3}0.247{col 60}{space 4}-.1071135{col 73}{space 3} .4155842
{txt}{space 12}income {c |}{col 20}{res}{space 2} .2840585{col 32}{space 2} .1162669{col 43}{space 1}    2.44{col 52}{space 3}0.015{col 60}{space 4} .0561796{col 73}{space 3} .5119373
{txt}{space 4}econ_wellbeing {c |}{col 20}{res}{space 2} .1893185{col 32}{space 2} .1143286{col 43}{space 1}    1.66{col 52}{space 3}0.098{col 60}{space 4}-.0347614{col 73}{space 3} .4133984
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2}-3.981256{col 32}{space 2} 1.448914{col 60}{space 4}-6.821075{col 73}{space 3}-1.141438
{txt}             /cut2 {c |}{col 20}{res}{space 2}-2.165087{col 32}{space 2} 1.124886{col 60}{space 4}-4.369824{col 73}{space 3} .0396494
{txt}             /cut3 {c |}{col 20}{res}{space 2}-1.013235{col 32}{space 2} 1.075652{col 60}{space 4}-3.121473{col 73}{space 3} 1.095004
{txt}             /cut4 {c |}{col 20}{res}{space 2}  1.85453{col 32}{space 2} 1.067904{col 60}{space 4} -.238523{col 73}{space 3} 3.947583
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. ********* Table 3
. ologit womenpolitics treatment

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3804.6391}  
Iteration 1:{space 3}log likelihood = {res:-3802.6287}  
Iteration 2:{space 3}log likelihood = {res:-3802.6285}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     2,709
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      4.02
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0449
{txt}Log likelihood = {res}-3802.6285{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}womenpolitics{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}treatment {c |}{col 15}{res}{space 2} .1395325{col 27}{space 2} .0696022{col 38}{space 1}    2.00{col 47}{space 3}0.045{col 55}{space 4} .0031146{col 68}{space 3} .2759503
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /cut1 {c |}{col 15}{res}{space 2}-.6355019{col 27}{space 2} .0534467{col 55}{space 4}-.7402555{col 68}{space 3}-.5307482
{txt}        /cut2 {c |}{col 15}{res}{space 2} .8191918{col 27}{space 2} .0543539{col 55}{space 4} .7126601{col 68}{space 3} .9257234
{txt}        /cut3 {c |}{col 15}{res}{space 2} 1.604567{col 27}{space 2} .0618654{col 55}{space 4} 1.483313{col 68}{space 3} 1.725821
{txt}        /cut4 {c |}{col 15}{res}{space 2} 3.443441{col 27}{space 2} .1133226{col 55}{space 4} 3.221333{col 68}{space 3} 3.665549
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit womenpolitics treatment if AKP==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1399.9647}  
Iteration 1:{space 3}log likelihood = {res:-1397.2999}  
Iteration 2:{space 3}log likelihood = {res: -1397.299}  
Iteration 3:{space 3}log likelihood = {res: -1397.299}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       946
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      5.33
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0209
{txt}Log likelihood = {res} -1397.299{txt}{col 49}Pseudo R2{col 67}= {res}    0.0019

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}womenpolitics{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}treatment {c |}{col 15}{res}{space 2} .2696832{col 27}{space 2} .1169217{col 38}{space 1}    2.31{col 47}{space 3}0.021{col 55}{space 4} .0405209{col 68}{space 3} .4988455
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /cut1 {c |}{col 15}{res}{space 2}-1.356483{col 27}{space 2} .1014712{col 55}{space 4}-1.555363{col 68}{space 3}-1.157603
{txt}        /cut2 {c |}{col 15}{res}{space 2} .2488334{col 27}{space 2} .0888811{col 55}{space 4} .0746295{col 68}{space 3} .4230372
{txt}        /cut3 {c |}{col 15}{res}{space 2} 1.167213{col 27}{space 2} .0969628{col 55}{space 4} .9771696{col 68}{space 3} 1.357257
{txt}        /cut4 {c |}{col 15}{res}{space 2} 3.169074{col 27}{space 2} .1678632{col 55}{space 4} 2.840068{col 68}{space 3}  3.49808
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit womenpolitics treatment if CHP==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-545.71727}  
Iteration 1:{space 3}log likelihood = {res:-544.70055}  
Iteration 2:{space 3}log likelihood = {res:-544.70022}  
Iteration 3:{space 3}log likelihood = {res:-544.70022}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       524
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      2.03
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1538
{txt}Log likelihood = {res}-544.70022{txt}{col 49}Pseudo R2{col 67}= {res}    0.0019

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}womenpolitics{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}treatment {c |}{col 15}{res}{space 2} .2451334{col 27}{space 2} .1720534{col 38}{space 1}    1.42{col 47}{space 3}0.154{col 55}{space 4} -.092085{col 68}{space 3} .5823518
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /cut1 {c |}{col 15}{res}{space 2} .4389563{col 27}{space 2} .1247435{col 55}{space 4} .1944634{col 68}{space 3} .6834491
{txt}        /cut2 {c |}{col 15}{res}{space 2}  2.19198{col 27}{space 2} .1659283{col 55}{space 4} 1.866767{col 68}{space 3} 2.517194
{txt}        /cut3 {c |}{col 15}{res}{space 2}   2.8275{col 27}{space 2} .2020056{col 55}{space 4} 2.431576{col 68}{space 3} 3.223423
{txt}        /cut4 {c |}{col 15}{res}{space 2} 4.067797{col 27}{space 2} .3324317{col 55}{space 4} 3.416243{col 68}{space 3} 4.719352
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit womenpolitics treatment fem religiosity eduord age age2 urbanrural income econ_wellbeing  

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3365.3472}  
Iteration 1:{space 3}log likelihood = {res:-3204.1327}  
Iteration 2:{space 3}log likelihood = {res:-3202.8809}  
Iteration 3:{space 3}log likelihood = {res:-3202.8795}  
Iteration 4:{space 3}log likelihood = {res:-3202.8795}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     2,397
{txt}{col 49}LR chi2({res}9{txt}){col 67}= {res}    324.94
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3202.8795{txt}{col 49}Pseudo R2{col 67}= {res}    0.0483

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} womenpolitics{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}treatment {c |}{col 16}{res}{space 2} .1972223{col 28}{space 2} .0753279{col 39}{space 1}    2.62{col 48}{space 3}0.009{col 56}{space 4} .0495824{col 69}{space 3} .3448622
{txt}{space 11}fem {c |}{col 16}{res}{space 2}-.8639287{col 28}{space 2} .0791362{col 39}{space 1}  -10.92{col 48}{space 3}0.000{col 56}{space 4}-1.019033{col 69}{space 3}-.7088245
{txt}{space 3}religiosity {c |}{col 16}{res}{space 2} .3876712{col 28}{space 2} .0612638{col 39}{space 1}    6.33{col 48}{space 3}0.000{col 56}{space 4} .2675964{col 69}{space 3}  .507746
{txt}{space 8}eduord {c |}{col 16}{res}{space 2}-.2534674{col 28}{space 2} .0342498{col 39}{space 1}   -7.40{col 48}{space 3}0.000{col 56}{space 4}-.3205958{col 69}{space 3} -.186339
{txt}{space 11}age {c |}{col 16}{res}{space 2}  .038064{col 28}{space 2} .0137842{col 39}{space 1}    2.76{col 48}{space 3}0.006{col 56}{space 4} .0110475{col 69}{space 3} .0650805
{txt}{space 10}age2 {c |}{col 16}{res}{space 2}-.0004233{col 28}{space 2} .0001494{col 39}{space 1}   -2.83{col 48}{space 3}0.005{col 56}{space 4}-.0007161{col 69}{space 3}-.0001305
{txt}{space 4}urbanrural {c |}{col 16}{res}{space 2} .0349491{col 28}{space 2} .0529655{col 39}{space 1}    0.66{col 48}{space 3}0.509{col 56}{space 4}-.0688614{col 69}{space 3} .1387596
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.2026054{col 28}{space 2} .0424704{col 39}{space 1}   -4.77{col 48}{space 3}0.000{col 56}{space 4}-.2858459{col 69}{space 3}-.1193649
{txt}econ_wellbeing {c |}{col 16}{res}{space 2}-.1500571{col 28}{space 2} .0432851{col 39}{space 1}   -3.47{col 48}{space 3}0.001{col 56}{space 4}-.2348943{col 69}{space 3}-.0652199
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         /cut1 {c |}{col 16}{res}{space 2}-1.156733{col 28}{space 2} .4351454{col 56}{space 4}-2.009603{col 69}{space 3}-.3038638
{txt}         /cut2 {c |}{col 16}{res}{space 2} .4972779{col 28}{space 2} .4345278{col 56}{space 4}-.3543808{col 69}{space 3} 1.348937
{txt}         /cut3 {c |}{col 16}{res}{space 2} 1.326926{col 28}{space 2} .4356338{col 56}{space 4} .4730994{col 69}{space 3} 2.180752
{txt}         /cut4 {c |}{col 16}{res}{space 2} 3.167127{col 28}{space 2} .4462208{col 56}{space 4}  2.29255{col 69}{space 3} 4.041704
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit womenpolitics treatment fem religiosity eduord age age2 urbanrural income econ_wellbeing  if AKP==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1273.9112}  
Iteration 1:{space 3}log likelihood = {res:-1228.3253}  
Iteration 2:{space 3}log likelihood = {res:-1227.9859}  
Iteration 3:{space 3}log likelihood = {res:-1227.9857}  
Iteration 4:{space 3}log likelihood = {res:-1227.9857}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       861
{txt}{col 49}LR chi2({res}9{txt}){col 67}= {res}     91.85
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1227.9857{txt}{col 49}Pseudo R2{col 67}= {res}    0.0361

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} womenpolitics{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}treatment {c |}{col 16}{res}{space 2} .3274254{col 28}{space 2} .1237809{col 39}{space 1}    2.65{col 48}{space 3}0.008{col 56}{space 4} .0848193{col 69}{space 3} .5700315
{txt}{space 11}fem {c |}{col 16}{res}{space 2}-1.072842{col 28}{space 2} .1345811{col 39}{space 1}   -7.97{col 48}{space 3}0.000{col 56}{space 4}-1.336616{col 69}{space 3}-.8090679
{txt}{space 3}religiosity {c |}{col 16}{res}{space 2}  .348118{col 28}{space 2} .1141018{col 39}{space 1}    3.05{col 48}{space 3}0.002{col 56}{space 4} .1244827{col 69}{space 3} .5717533
{txt}{space 8}eduord {c |}{col 16}{res}{space 2}-.1797084{col 28}{space 2} .0598239{col 39}{space 1}   -3.00{col 48}{space 3}0.003{col 56}{space 4}-.2969611{col 69}{space 3}-.0624556
{txt}{space 11}age {c |}{col 16}{res}{space 2} .0245824{col 28}{space 2} .0246119{col 39}{space 1}    1.00{col 48}{space 3}0.318{col 56}{space 4} -.023656{col 69}{space 3} .0728208
{txt}{space 10}age2 {c |}{col 16}{res}{space 2}-.0002834{col 28}{space 2} .0002623{col 39}{space 1}   -1.08{col 48}{space 3}0.280{col 56}{space 4}-.0007975{col 69}{space 3} .0002308
{txt}{space 4}urbanrural {c |}{col 16}{res}{space 2} .1499843{col 28}{space 2} .0872784{col 39}{space 1}    1.72{col 48}{space 3}0.086{col 56}{space 4}-.0210782{col 69}{space 3} .3210467
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.1023557{col 28}{space 2} .0722103{col 39}{space 1}   -1.42{col 48}{space 3}0.156{col 56}{space 4}-.2438853{col 69}{space 3}  .039174
{txt}econ_wellbeing {c |}{col 16}{res}{space 2}-.1117737{col 28}{space 2}  .081143{col 39}{space 1}   -1.38{col 48}{space 3}0.168{col 56}{space 4}-.2708111{col 69}{space 3} .0472637
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         /cut1 {c |}{col 16}{res}{space 2}-1.307847{col 28}{space 2} .7988583{col 56}{space 4}-2.873581{col 69}{space 3} .2578862
{txt}         /cut2 {c |}{col 16}{res}{space 2} .4582133{col 28}{space 2} .7979259{col 56}{space 4}-1.105693{col 69}{space 3} 2.022119
{txt}         /cut3 {c |}{col 16}{res}{space 2} 1.414609{col 28}{space 2} .7994781{col 56}{space 4} -.152339{col 69}{space 3} 2.981558
{txt}         /cut4 {c |}{col 16}{res}{space 2} 3.398192{col 28}{space 2} .8113841{col 56}{space 4} 1.807909{col 69}{space 3} 4.988476
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit womenpolitics treatment fem religiosity eduord age age2 urbanrural income econ_wellbeing  if CHP==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:  -487.685}  
Iteration 1:{space 3}log likelihood = {res:-459.73233}  
Iteration 2:{space 3}log likelihood = {res:-459.36794}  
Iteration 3:{space 3}log likelihood = {res:-459.36753}  
Iteration 4:{space 3}log likelihood = {res:-459.36753}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       471
{txt}{col 49}LR chi2({res}9{txt}){col 67}= {res}     56.63
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-459.36753{txt}{col 49}Pseudo R2{col 67}= {res}    0.0581

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} womenpolitics{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}treatment {c |}{col 16}{res}{space 2} .3324662{col 28}{space 2} .1887075{col 39}{space 1}    1.76{col 48}{space 3}0.078{col 56}{space 4}-.0373938{col 69}{space 3} .7023262
{txt}{space 11}fem {c |}{col 16}{res}{space 2}-.9662155{col 28}{space 2} .1976185{col 39}{space 1}   -4.89{col 48}{space 3}0.000{col 56}{space 4}-1.353541{col 69}{space 3}-.5788902
{txt}{space 3}religiosity {c |}{col 16}{res}{space 2} .0341558{col 28}{space 2} .1531082{col 39}{space 1}    0.22{col 48}{space 3}0.823{col 56}{space 4}-.2659307{col 69}{space 3} .3342423
{txt}{space 8}eduord {c |}{col 16}{res}{space 2}-.1901144{col 28}{space 2} .0849651{col 39}{space 1}   -2.24{col 48}{space 3}0.025{col 56}{space 4}-.3566429{col 69}{space 3}-.0235859
{txt}{space 11}age {c |}{col 16}{res}{space 2} .0284644{col 28}{space 2} .0324507{col 39}{space 1}    0.88{col 48}{space 3}0.380{col 56}{space 4}-.0351378{col 69}{space 3} .0920665
{txt}{space 10}age2 {c |}{col 16}{res}{space 2}-.0002875{col 28}{space 2} .0003456{col 39}{space 1}   -0.83{col 48}{space 3}0.405{col 56}{space 4}-.0009648{col 69}{space 3} .0003898
{txt}{space 4}urbanrural {c |}{col 16}{res}{space 2}  .079723{col 28}{space 2} .1294606{col 39}{space 1}    0.62{col 48}{space 3}0.538{col 56}{space 4}-.1740152{col 69}{space 3} .3334612
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.4484502{col 28}{space 2} .1130829{col 39}{space 1}   -3.97{col 48}{space 3}0.000{col 56}{space 4}-.6700885{col 69}{space 3}-.2268119
{txt}econ_wellbeing {c |}{col 16}{res}{space 2}-.0894096{col 28}{space 2} .1070318{col 39}{space 1}   -0.84{col 48}{space 3}0.404{col 56}{space 4}-.2991882{col 69}{space 3} .1203689
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         /cut1 {c |}{col 16}{res}{space 2}-1.571376{col 28}{space 2} 1.022463{col 56}{space 4}-3.575367{col 69}{space 3} .4326157
{txt}         /cut2 {c |}{col 16}{res}{space 2} .4068162{col 28}{space 2} 1.021213{col 56}{space 4}-1.594724{col 69}{space 3} 2.408356
{txt}         /cut3 {c |}{col 16}{res}{space 2} 1.095112{col 28}{space 2} 1.026685{col 56}{space 4}-.9171541{col 69}{space 3} 3.107378
{txt}         /cut4 {c |}{col 16}{res}{space 2} 2.432615{col 28}{space 2} 1.067338{col 56}{space 4} .3406712{col 69}{space 3} 4.524559
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/4_/2pllzwnn7q3b3r7q838pffsr0000gn/T//SD39641.000000"
{txt}
{com}. 
. 
. * Substantive Impact using Clarify
. ************ Equality of Opportunity (First DV)
. estsimp ologit equalofopportunity fem religiosity eduord age age2 urbanrural income econ_wellbeing treatment 

{txt}Iteration 0:   log likelihood = {res} -2658.166
{txt}Iteration 1:   log likelihood = {res}-2545.6015
{txt}Iteration 2:   log likelihood = {res}-2544.8879
{txt}Iteration 3:   log likelihood = {res}-2544.8873

{txt}Ordered logit estimates                           Number of obs   = {res}      2402
                                                  {txt}LR chi2({res}9{txt})      = {res}    226.56
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log likelihood = {res}-2544.8873                       {txt}Pseudo R2       = {res}    0.0426

{txt}{hline 13}{c TT}{hline 64}
equalofopp~y {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
         fem {c |}  {res} .7059406    .082719     8.53   0.000     .5438142    .8680669
 {txt}religiosity {c |}  {res}-.3547097   .0635638    -5.58   0.000    -.4792924    -.230127
      {txt}eduord {c |}  {res} .2130905    .035884     5.94   0.000     .1427591    .2834218
         {txt}age {c |}  {res}-.0528158   .0144033    -3.67   0.000    -.0810459   -.0245858
        {txt}age2 {c |}  {res} .0005647   .0001555     3.63   0.000     .0002599    .0008695
  {txt}urbanrural {c |}  {res} .0384492   .0553276     0.69   0.487     -.069991    .1468894
      {txt}income {c |}  {res} .1364111   .0444225     3.07   0.002     .0493446    .2234776
{txt}econ_wellb~g {c |}  {res} .0993035   .0451342     2.20   0.028     .0108421     .187765
   {txt}treatment {c |}  {res}-.2058202   .0792434    -2.60   0.009    -.3611345    -.050506
{txt}{hline 13}{c +}{hline 64}
       _cut1 {c |}  {res}-5.273407   .5095427          {txt}(Ancillary parameters)
       _cut2 {c |}  {res}-3.113492   .4619023 
       {txt}_cut3 {c |}  {res}-2.265251   .4581165 
       {txt}_cut4 {c |}  {res}-.0176577   .4557449 
{txt}{hline 13}{c BT}{hline 64}

{res}Simulating main parameters.  Please wait....
% of simulations completed: 7% 15% 23% 30% 38% 46% 53% 61% 69% 76% 84% 92% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13
{txt}
{com}. setx mean 
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(equalo~y=1) |  {res} .0068678     .0016165      .004202     .010586
            {txt}Pr(equalo~y=2) |  {res}  .048176     .0041908     .0403165     .056793
            {txt}Pr(equalo~y=3) |  {res} .0644588      .005062     .0543758    .0745167
            {txt}Pr(equalo~y=4) |  {res} .4422026     .0109767     .4199748    .4631525
            {txt}Pr(equalo~y=5) |  {res} .4382948     .0109103      .416401    .4603054
{txt}
{com}. simqi, fd(pr) changex(treatment 0 1) level(90)

{res}First Difference: treatment 0 1

{txt}      Quantity of Interest |     Mean       Std. Err.    [90% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(equalo~y = 1) |  {res} .0014162     .0006401     .0004124    .0025632
         {txt}dPr(equalo~y = 2) |  {res} .0093399     .0035648     .0033023    .0151463
         {txt}dPr(equalo~y = 3) |  {res}  .010989     .0041985     .0039836    .0177597
         {txt}dPr(equalo~y = 4) |  {res} .0290728     .0108343     .0106803    .0467397
         {txt}dPr(equalo~y = 5) |  {res}-.0508179     .0188827    -.0812229   -.0184244
{txt}
{com}. * Drop the simulated parameters to use the "estsimp" for the second DV
. drop b1-b13
{txt}
{com}. 
. 
. estsimp ologit equalofopportunity fem religiosity eduord age age2 urbanrural income econ_wellbeing treatment if CHP==1

{txt}Iteration 0:   log likelihood = {res}-380.94876
{txt}Iteration 1:   log likelihood = {res}-354.20148
{txt}Iteration 2:   log likelihood = {res}-353.76202
{txt}Iteration 3:   log likelihood = {res}-353.76131

{txt}Ordered logit estimates                           Number of obs   = {res}       471
                                                  {txt}LR chi2({res}9{txt})      = {res}     54.37
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log likelihood = {res}-353.76131                       {txt}Pseudo R2       = {res}    0.0714

{txt}{hline 13}{c TT}{hline 64}
equalofopp~y {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
         fem {c |}  {res} .9462729    .210052     4.50   0.000     .5345786    1.357967
 {txt}religiosity {c |}  {res}-.2723232   .1620284    -1.68   0.093     -.589893    .0452467
      {txt}eduord {c |}  {res} .2617173   .0917119     2.85   0.004     .0819654    .4414693
         {txt}age {c |}  {res} -.002451   .0344609    -0.07   0.943     -.069993    .0650911
        {txt}age2 {c |}  {res}-6.31e-06   .0003657    -0.02   0.986    -.0007231    .0007105
  {txt}urbanrural {c |}  {res} .1542353   .1333435     1.16   0.247    -.1071132    .4155839
      {txt}income {c |}  {res} .2840585   .1162667     2.44   0.015     .0561799     .511937
{txt}econ_wellb~g {c |}  {res} .1893185   .1143284     1.66   0.098    -.0347611    .4133981
   {txt}treatment {c |}  {res}-.3525676   .2003287    -1.76   0.078    -.7452048    .0400695
{txt}{hline 13}{c +}{hline 64}
       _cut1 {c |}  {res}-3.981256   1.448913          {txt}(Ancillary parameters)
       _cut2 {c |}  {res}-2.165087   1.124885 
       {txt}_cut3 {c |}  {res}-1.013235    1.07565 
       {txt}_cut4 {c |}  {res}  1.85453   1.067902 
{txt}{hline 13}{c BT}{hline 64}

{res}Simulating main parameters.  Please wait....
% of simulations completed: 7% 15% 23% 30% 38% 46% 53% 61% 69% 76% 84% 92% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13
{txt}
{com}. setx mean 
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(equalo~y=1) |  {res} .0026829     .0037204     .0002136    .0126113
            {txt}Pr(equalo~y=2) |  {res} .0081523     .0048409     .0006116     .019021
            {txt}Pr(equalo~y=3) |  {res} .0204512     .0065615     .0087521    .0350533
            {txt}Pr(equalo~y=4) |  {res} .3221202     .0233842     .2795158    .3708625
            {txt}Pr(equalo~y=5) |  {res} .6465934     .0242346     .5970682    .6924297
{txt}
{com}. simqi, fd(pr) changex(treatment 0 1) level(90)

{res}First Difference: treatment 0 1

{txt}      Quantity of Interest |     Mean       Std. Err.    [90% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(equalo~y = 1) |  {res}  .000968     .0016132     .0000262    .0034169
         {txt}dPr(equalo~y = 2) |  {res} .0028789     .0026136    -.0001391    .0074896
         {txt}dPr(equalo~y = 3) |  {res}  .006969     .0046458     .0005662    .0151967
         {txt}dPr(equalo~y = 4) |  {res} .0692439     .0390577     .0075112     .131675
         {txt}dPr(equalo~y = 5) |  {res}-.0800599     .0451669    -.1536158   -.0090096
{txt}
{com}. drop b1-b13
{txt}
{com}. 
. 
. * Divide the values of "First Difference" for each answer (i.e., absolutely wrong, wrong, etc.) by the baseline probability of the
. * respective answer to calculate the percentage change. For instance, among the CHP voters, the treatment condition reduces the
. * likelihood of saying "absolutely correct" for the first statement (i.e., "equality of opportunity") by 12.7 percent (-.0817387/.6471322)
. * Note that each time we use the "estsimp" command we will get slightly different results as these results are based on simulated probabilities.
. 
. estsimp ologit equalofopportunity fem religiosity eduord age age2 urbanrural income econ_wellbeing treatment if AKP==1

{txt}Iteration 0:   log likelihood = {res}-1048.9945
{txt}Iteration 1:   log likelihood = {res} -1021.847
{txt}Iteration 2:   log likelihood = {res}-1021.7035
{txt}Iteration 3:   log likelihood = {res}-1021.7034

{txt}Ordered logit estimates                           Number of obs   = {res}       862
                                                  {txt}LR chi2({res}9{txt})      = {res}     54.58
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log likelihood = {res}-1021.7034                       {txt}Pseudo R2       = {res}    0.0260

{txt}{hline 13}{c TT}{hline 64}
equalofopp~y {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
         fem {c |}  {res} .7432236   .1373845     5.41   0.000     .4739548    1.012492
 {txt}religiosity {c |}  {res}-.3147745   .1159124    -2.72   0.007    -.5419588   -.0875903
      {txt}eduord {c |}  {res} .0832528   .0623427     1.34   0.182    -.0389366    .2054423
         {txt}age {c |}  {res}-.0513301   .0251755    -2.04   0.041    -.1006733    -.001987
        {txt}age2 {c |}  {res} .0005274   .0002661     1.98   0.047     5.93e-06    .0010489
  {txt}urbanrural {c |}  {res}-.1408255   .0906869    -1.55   0.120    -.3185685    .0369176
      {txt}income {c |}  {res} .1378942   .0750532     1.84   0.066    -.0092073    .2849957
{txt}econ_wellb~g {c |}  {res} .0859478   .0826383     1.04   0.298    -.0760204    .2479159
   {txt}treatment {c |}  {res} -.345402   .1289376    -2.68   0.007     -.598115   -.0926889
{txt}{hline 13}{c +}{hline 64}
       _cut1 {c |}  {res}-6.122124   .9018289          {txt}(Ancillary parameters)
       _cut2 {c |}  {res}-3.655549   .8358596 
       {txt}_cut3 {c |}  {res}-2.767201   .8314099 
       {txt}_cut4 {c |}  {res}-.6441154   .8262735 
{txt}{hline 13}{c BT}{hline 64}

{res}Simulating main parameters.  Please wait....
% of simulations completed: 7% 15% 23% 30% 38% 46% 53% 61% 69% 76% 84% 92% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13
{txt}
{com}. setx mean 
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(equalo~y=1) |  {res}  .008761     .0032356     .0042108    .0161199
            {txt}Pr(equalo~y=2) |  {res} .0814397     .0090257     .0649193    .1001144
            {txt}Pr(equalo~y=3) |  {res} .1035934     .0100609     .0832617    .1243998
            {txt}Pr(equalo~y=4) |  {res} .4728758     .0172478     .4393689    .5053018
            {txt}Pr(equalo~y=5) |  {res}   .33333      .015821     .3032768    .3629699
{txt}
{com}. simqi, fd(pr) changex(treatment 0 1) level(90)

{res}First Difference: treatment 0 1

{txt}      Quantity of Interest |     Mean       Std. Err.    [90% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(equalo~y = 1) |  {res} .0029727     .0016414     .0008416    .0062154
         {txt}dPr(equalo~y = 2) |  {res}  .024973     .0099146     .0092646    .0418175
         {txt}dPr(equalo~y = 3) |  {res} .0252297     .0098157     .0089084    .0414067
         {txt}dPr(equalo~y = 4) |  {res} .0230004     .0096129     .0078878    .0395322
         {txt}dPr(equalo~y = 5) |  {res}-.0761758     .0289917     -.125362   -.0280948
{txt}
{com}. drop b1-b13
{txt}
{com}. 
. 
. ************ Women's Presence in Politics (Second DV)
. estsimp ologit womenpolitics fem religiosity eduord age age2 urbanrural income econ_wellbeing treatment 

{txt}Iteration 0:   log likelihood = {res}-3365.3472
{txt}Iteration 1:   log likelihood = {res}-3204.1327
{txt}Iteration 2:   log likelihood = {res}-3202.8809
{txt}Iteration 3:   log likelihood = {res}-3202.8795

{txt}Ordered logit estimates                           Number of obs   = {res}      2397
                                                  {txt}LR chi2({res}9{txt})      = {res}    324.94
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log likelihood = {res}-3202.8795                       {txt}Pseudo R2       = {res}    0.0483

{txt}{hline 13}{c TT}{hline 64}
womenpolit~s {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
         fem {c |}  {res}-.8639287   .0791362   -10.92   0.000    -1.019033   -.7088246
 {txt}religiosity {c |}  {res} .3876712   .0612638     6.33   0.000     .2675964    .5077459
      {txt}eduord {c |}  {res}-.2534674   .0342498    -7.40   0.000    -.3205958   -.1863391
         {txt}age {c |}  {res}  .038064   .0137842     2.76   0.006     .0110475    .0650805
        {txt}age2 {c |}  {res}-.0004233   .0001494    -2.83   0.005    -.0007161   -.0001305
  {txt}urbanrural {c |}  {res} .0349491   .0529655     0.66   0.509    -.0688614    .1387595
      {txt}income {c |}  {res}-.2026054   .0424704    -4.77   0.000    -.2858458    -.119365
{txt}econ_wellb~g {c |}  {res}-.1500571   .0432851    -3.47   0.001    -.2348943     -.06522
   {txt}treatment {c |}  {res} .1972223   .0753279     2.62   0.009     .0495824    .3448622
{txt}{hline 13}{c +}{hline 64}
       _cut1 {c |}  {res}-1.156733   .4351453          {txt}(Ancillary parameters)
       _cut2 {c |}  {res} .4972779   .4345276 
       {txt}_cut3 {c |}  {res} 1.326926   .4356336 
       {txt}_cut4 {c |}  {res} 3.167127   .4462207 
{txt}{hline 13}{c BT}{hline 64}

{res}Simulating main parameters.  Please wait....
% of simulations completed: 7% 15% 23% 30% 38% 46% 53% 61% 69% 76% 84% 92% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13
{txt}
{com}. setx mean 
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(womenp~s=1) |  {res}  .309523     .0099324      .289639    .3280003
            {txt}Pr(womenp~s=2) |  {res}  .391285      .010724     .3712053     .412455
            {txt}Pr(womenp~s=3) |  {res} .1419492     .0074354     .1274364    .1569179
            {txt}Pr(womenp~s=4) |  {res}  .128438     .0066455     .1160605    .1418218
            {txt}Pr(womenp~s=5) |  {res} .0288048      .003184     .0234168    .0355084
{txt}
{com}. simqi, fd(pr) changex(treatment 0 1) level(90)

{res}First Difference: treatment 0 1

{txt}      Quantity of Interest |     Mean       Std. Err.    [90% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(womenp~s = 1) |  {res}-.0419179     .0160573    -.0679755   -.0152607
         {txt}dPr(womenp~s = 2) |  {res}  .000829     .0013683    -.0011934     .003124
         {txt}dPr(womenp~s = 3) |  {res} .0151087     .0058502     .0054202    .0244139
         {txt}dPr(womenp~s = 4) |  {res} .0204822     .0078766     .0077035    .0334086
         {txt}dPr(womenp~s = 5) |  {res}  .005498     .0022073      .001953     .009263
{txt}
{com}. drop b1-b13
{txt}
{com}. 
. 
. estsimp ologit womenpolitics fem religiosity eduord age age2 urbanrural income econ_wellbeing treatment if CHP==1

{txt}Iteration 0:   log likelihood = {res}  -487.685
{txt}Iteration 1:   log likelihood = {res}-459.73233
{txt}Iteration 2:   log likelihood = {res}-459.36794
{txt}Iteration 3:   log likelihood = {res}-459.36753

{txt}Ordered logit estimates                           Number of obs   = {res}       471
                                                  {txt}LR chi2({res}9{txt})      = {res}     56.63
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log likelihood = {res}-459.36753                       {txt}Pseudo R2       = {res}    0.0581

{txt}{hline 13}{c TT}{hline 64}
womenpolit~s {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
         fem {c |}  {res}-.9662155   .1976184    -4.89   0.000     -1.35354   -.5788905
 {txt}religiosity {c |}  {res} .0341558   .1531081     0.22   0.823    -.2659306    .3342422
      {txt}eduord {c |}  {res}-.1901144    .084965    -2.24   0.025    -.3566428    -.023586
         {txt}age {c |}  {res} .0284644   .0324507     0.88   0.380    -.0351378    .0920665
        {txt}age2 {c |}  {res}-.0002875   .0003456    -0.83   0.405    -.0009648    .0003898
  {txt}urbanrural {c |}  {res}  .079723   .1294606     0.62   0.538     -.174015    .3334611
      {txt}income {c |}  {res}-.4484502   .1130828    -3.97   0.000    -.6700884    -.226812
{txt}econ_wellb~g {c |}  {res}-.0894096   .1070318    -0.84   0.404     -.299188    .1203687
   {txt}treatment {c |}  {res} .3324662   .1887074     1.76   0.078    -.0373936     .702326
{txt}{hline 13}{c +}{hline 64}
       _cut1 {c |}  {res}-1.571376   1.022463          {txt}(Ancillary parameters)
       _cut2 {c |}  {res} .4068162   1.021212 
       {txt}_cut3 {c |}  {res} 1.095112   1.026684 
       {txt}_cut4 {c |}  {res} 2.432615   1.067337 
{txt}{hline 13}{c BT}{hline 64}

{res}Simulating main parameters.  Please wait....
% of simulations completed: 7% 15% 23% 30% 38% 46% 53% 61% 69% 76% 84% 92% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13
{txt}
{com}. setx mean 
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(womenp~s=1) |  {res}  .577268     .0238854     .5285328    .6244971
            {txt}Pr(womenp~s=2) |  {res} .3297716     .0227176     .2870475    .3751533
            {txt}Pr(womenp~s=3) |  {res} .0437836     .0088916     .0276981    .0615667
            {txt}Pr(womenp~s=4) |  {res}  .035319     .0081231     .0209655    .0529175
            {txt}Pr(womenp~s=5) |  {res} .0138578     .0048132     .0067489    .0250166
{txt}
{com}. simqi, fd(pr) changex(treatment 0 1) level(90)

{res}First Difference: treatment 0 1

{txt}      Quantity of Interest |     Mean       Std. Err.    [90% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(womenp~s = 1) |  {res}-.0789131     .0432934    -.1485121   -.0071132
         {txt}dPr(womenp~s = 2) |  {res} .0513743     .0283777     .0046552      .09623
         {txt}dPr(womenp~s = 3) |  {res} .0122609     .0072223     .0009024      .02458
         {txt}dPr(womenp~s = 4) |  {res} .0107964     .0066121     .0010276    .0220729
         {txt}dPr(womenp~s = 5) |  {res} .0044814     .0029554     .0003519    .0096096
{txt}
{com}. drop b1-b13
{txt}
{com}. 
. 
. estsimp ologit womenpolitics fem religiosity eduord age age2 urbanrural income econ_wellbeing treatment if AKP==1

{txt}Iteration 0:   log likelihood = {res}-1273.9112
{txt}Iteration 1:   log likelihood = {res}-1228.3253
{txt}Iteration 2:   log likelihood = {res}-1227.9859
{txt}Iteration 3:   log likelihood = {res}-1227.9857

{txt}Ordered logit estimates                           Number of obs   = {res}       861
                                                  {txt}LR chi2({res}9{txt})      = {res}     91.85
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log likelihood = {res}-1227.9857                       {txt}Pseudo R2       = {res}    0.0361

{txt}{hline 13}{c TT}{hline 64}
womenpolit~s {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
         fem {c |}  {res}-1.072842   .1345811    -7.97   0.000    -1.336616    -.809068
 {txt}religiosity {c |}  {res}  .348118   .1141017     3.05   0.002     .1244827    .5717533
      {txt}eduord {c |}  {res}-.1797084   .0598239    -3.00   0.003    -.2969611   -.0624556
         {txt}age {c |}  {res} .0245824   .0246119     1.00   0.318     -.023656    .0728208
        {txt}age2 {c |}  {res}-.0002834   .0002623    -1.08   0.280    -.0007975    .0002308
  {txt}urbanrural {c |}  {res} .1499843   .0872783     1.72   0.086    -.0210782    .3210467
      {txt}income {c |}  {res}-.1023557   .0722103    -1.42   0.156    -.2438853    .0391739
{txt}econ_wellb~g {c |}  {res}-.1117737    .081143    -1.38   0.168    -.2708111    .0472637
   {txt}treatment {c |}  {res} .3274254   .1237809     2.65   0.008     .0848193    .5700315
{txt}{hline 13}{c +}{hline 64}
       _cut1 {c |}  {res}-1.307847   .7988582          {txt}(Ancillary parameters)
       _cut2 {c |}  {res} .4582133   .7979258 
       {txt}_cut3 {c |}  {res} 1.414609    .799478 
       {txt}_cut4 {c |}  {res} 3.398192    .811384 
{txt}{hline 13}{c BT}{hline 64}

{res}Simulating main parameters.  Please wait....
% of simulations completed: 7% 15% 23% 30% 38% 46% 53% 61% 69% 76% 84% 92% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13
{txt}
{com}. setx mean 
{txt}
{com}. simqi, pr

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(womenp~s=1) |  {res} .1684219     .0131247     .1429597    .1939511
            {txt}Pr(womenp~s=2) |  {res} .3736926     .0173127     .3416358    .4096349
            {txt}Pr(womenp~s=3) |  {res} .2131691     .0150846      .183347    .2415745
            {txt}Pr(womenp~s=4) |  {res}  .201891      .013749     .1756256    .2281526
            {txt}Pr(womenp~s=5) |  {res} .0428255     .0064154     .0318251    .0569732
{txt}
{com}. simqi, fd(pr) changex(treatment 0 1) level(90)

{res}First Difference: treatment 0 1

{txt}      Quantity of Interest |     Mean       Std. Err.    [90% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(womenp~s = 1) |  {res}-.0455255      .017485    -.0747835   -.0184575
         {txt}dPr(womenp~s = 2) |  {res}-.0344939     .0137047    -.0585556   -.0139769
         {txt}dPr(womenp~s = 3) |  {res} .0205024     .0080964     .0081318    .0343964
         {txt}dPr(womenp~s = 4) |  {res} .0462973     .0178964     .0189785    .0768107
         {txt}dPr(womenp~s = 5) |  {res} .0132197     .0055042     .0052261    .0229766
{txt}
{com}. drop b1-b13
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/4_/2pllzwnn7q3b3r7q838pffsr0000gn/T//SD39641.000000"
{txt}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/tevfikmuratyildirim/Dropbox/RESEARCH/A. T. Bulut & T. M. Yildirim/Survey Experiment - Konda/Elite Influence on Gender Egalitarianism--Replication.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 6 May 2021, 20:58:24
{txt}{.-}
{smcl}
{txt}{sf}{ul off}